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Pengaruh Gaya Kepemimpinan dan Fasilitas Kerja Terhadap Pegawai Pusat Pemberitaan Lembaga Penyiaran Publik Radio Republik Indonesia
No full textThis study was conducted on employees of the News Center of the Public Broadcasting Institution Radio Republik Indonesia (LPP RRI), which plays a strategic role in delivering information to the public. The purpose of this study was to examine the effect of leadership style and work facilities on employee job satisfaction. This research employed a quantitative approach with an associative research design. Data were collected through the distribution of questionnaires to 55 respondents selected using the Slovin formula from a total population of 112 employees. The data were analyzed using multiple linear regression with the assistance of SPSS version 29. The results show that leadership style has a significant effect on job satisfaction with a t-value of 5.780, while work facilities also have a significant effect with a t-value of 3.488. Simultaneously, both variables significantly affect job satisfaction with an F-value of 48.164. These findings confirm that improvements in leadership quality and the provision of adequate work facilities can have a positive impact on employee job satisfaction in the News Center of LPP RRI.Penelitian ini dilakukan pada pegawai Pusat Pemberitaan Lembaga Penyiaran Publik Radio Republik Indonesia (LPP RRI), yang menjadi objek penelitian karena perannya yang penting dalam menyajikan informasi kepada publik. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh gaya kepemimpinan dan fasilitas kerja terhadap kepuasan kerja pegawai. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis penelitian asosiatif. Teknik pengumpulan data dilakukan melalui penyebaran kuesioner kepada 55 responden yang telah dipilih menggunakan rumus Slovin dari populasi sebanyak 112 pegawai. Data yang diperoleh dianalisis menggunakan regresi linier berganda dengan bantuan aplikasi SPSS versi 29. Hasil penelitian menunjukkan bahwa baik gaya kepemimpinan maupun fasilitas kantor secara parsial maupun simultan berpengaruh positif dan signifikan terhadap kepuasan kerja pegawai. Temuan ini menegaskan bahwa peningkatan pada aspek kepemimpinan serta penyediaan fasilitas yang memadai dapat memberikan dampak positif terhadap kepuasan kerja di lingkungan Pusat Pemberitaan LPP RRI
MULTIPLAYER ONLINE ROLE-PLAYING GAME VIRTUAL CLASSROOMS USING THE GAME DEVELOPMENT LIFE CYCLE METHOD
Full text linkThe COVID-19 pandemic has disrupted traditional education, forcing a shift toward online learning, which often lacks engagement and effectiveness. Existing virtual classroom methods struggle to sustain students' attention and motivation, leading to reduced learning outcomes. To address these issues, this study develops an innovative Virtual Classroom application based on Multiplayer Online Role-Playing Game (MORPG) technology. The goal is to provide a more interactive and immersive learning environment, enhancing engagement among students and lecturers. Using the Unity Game Engine, Photon Unity Networking (PUN), and Photon Voice libraries, this application transforms online classes into game-like experiences. The development followed the Game Development Life Cycle (GDLC) methodology, ensuring a structured and effective approach. Blackbox testing confirmed that all functions operated as intended, while usability testing with the System Usability Scale (SUS) among 30 users yielded an average score of 71.92, indicating a satisfactory experience. The results demonstrate the application's potential to make online learning more appealing and effective, contributing a novel solution for remote education challenges by integrating gaming elements into the learning process
DESIGN OF FIRE EXTINGUISHER ROBOT USING IOT WITH ANDROID APPLICATION CONTROL
Full text linkFire is an unsupervised incidental disaster. This disaster has a detrimental impact on living and non-living things in the surrounding environment. This study was conducted to design an intelligent firefighting robot using Arduino Mega 2560 and Android-based IoT technology. This firefighting robot uses several Node MCU ESP8266 components as additional devices to connect to wifi. The L298N module regulates the speed and direction of the DC motor rotation, followed by the L9110 fan as hardware to extinguish the fire. The mobile robot prototype uses a DC motor as its driver. In addition, an Android application has been programmed to control the firefighting robot. This application has features that allow the robot to move in various directions and adjust the fan speed when extinguishing fires, all through an internet network connection. The study results showed that the application can be connected within a distance of 1-8 meters with good network quality. The test results showed that at a distance of 1-28 cm, the fan worked very well according to its function, and the Android application also worked optimally. In that range, the fan can extinguish the simulated fire source. The results of this study obtained a new approach to autonomous fire detection and extinguishing using IoT and robotic technology. In addition, it is able to integrate an Android-based IoT controller to enable remote control with real-time monitoring to overcome problems in previous research
ENHANCING HERBAL PLANT LEAF IMAGE DETECTION ACCURACY THROUGH MOBILENET ARCHITECTURE OPTIMIZATION IN CNN
Full text linkHerbal plants have various health benefits, but their type identification remains challenging for the general public. This study aims to improve the accuracy of herbal plant leaf classification using Convolutional Neural Network (CNN) based on MobileNetV2 architecture. To enhance model performance, various optimization techniques including fine-tuning, batch normalization, dropout, and learning rate scheduling were implemented. The experimental results showed that the proposed optimized model achieved an accuracy of 100%, significantly outperforming previous studies that used standard MobileNet with an accuracy of 86.7%. While these perfect results warrant additional validation with more diverse datasets to confirm generalizability, this study contributes to the development of a more accurate herbal plant classification system that is readily accessible to the general public. Future work should explore model performance under varying environmental conditions and with expanded plant species datasets
DIGITAL MARKETING MELALUI PEMBUATAN KONTEN PADA MEDIA SOSIAL SEBAGAI PENINGKATAN PROMOSI DAN BRANDING SEKOLAH
Full text linkDigital marketing has become one of the forms of marketing by utilizing digital platforms and technologies to connect with the target audience online. The use of digital marketing can be applied through digital-based media. Social media is integrated with various lines and used as a means of disseminating relevant content according to the target audience in achieving the organization's vision, mission, and goals. Based on the observation results, many teachers still have limited skills in creating digital content and marketing content. Therefore, the purpose of this community service activity is to enhance the marketing skills of teachers and educational staff through e-marketing-based activities for school social media. This community service activity was conducted in the Auditorium of Campus 3, Universitas Negeri Malang, Kepanjen Kidul, Kota Blitar. The partners in this community service activity are the PAUD teachers of Group 3, Kepanjen Kidul, Blitar City. The implementation method consists of three stages: needs analysis, execution, and evaluation. The result of the implementation of this program is content published in each institution as a means of promotion and branding. In addition, the PAUD teachers of Cluster 3 Kepanjen Kidul, Blitar City, gained knowledge and understanding both theoretically and practically, including understanding branding, design skills, website utilization, institutional branding enhancement, and skill sustainability. This understanding is expected to be continuously applied as an effort to optimize digital skills in the context of school branding at the early childhood education level
IMPLEMENTATION OF K-MEDOIDS METHOD FOR HEART DISEASE PREDICTION USING QUANTUM COMPUTING AND MANHATTAN DISTANCE
Full text linkHeart disease is a severe health condition characterized by dysfunctions in the heart and blood vessels, which can be fatal if not properly managed. Early detection and prediction of heart disease are crucial for understanding the prevalence and determining patients' quality of life. In this study, quantum computing is applied to enhance the performance of the K-Medoids method. A comparative analysis of these methods is conducted, focusing on their performance. The investigation utilizes a dataset of heart disease patient medical records. This dataset includes various attributes used to predict heart disease patterns. The dataset is tested using both the classical and K-Medoids methods with a quantum computing approach, employing Manhattan distance calculations. This study's findings reveal that applying quantum computing to the K-Medoids method results in clustering accuracy stability of 85%, equivalent to the classical method. Although there is no increase in accuracy, the quantum computing approach demonstrates potential improvements in data processing efficiency. These results highlight that the K-Medoids method with a quantum computing approach can contribute significantly to faster and more efficient medical data analysis. However, further research is needed for optimization and testing on more extensive and more diverse datasets
PENGEMBANGAN SISTEM INFORMASI MONITORING HARIAN MAGANG INDUSTRI PENDIDIKAN TEKNIK MESIN MENGGUNAKAN MODEL 4D
Full text linkThe problems encountered in the industrial internship courses that are held conventionally are less actual daily log book recording and limited frequency of monitoring supervisors, resulting in less than optimal internship results. This research aims to develop a daily monitoring information system for industrial internships for Mechanical Engineering Education students at Sebelas Maret University. The research method used is Research and Development (RnD) with 4D models including Define, Design, Develop, and Disseminate. The result of this study is to develop a web-based daily monitoring information system for industrial internships and accommodate the industrial internship process from the preparation stage to the end of the assessment. Based on the analysis results, this information system obtained the "good" category with an average website performance test score of 84.25 on 4 test tools. The system was also rated "highly valid" based on a 98.6% eligibility score from 3 IT experts and 85.6% from 30 trial students
PERFORM COMPARATION OF DEEP LEARNING METHODS IN GENDER CLASSIFICATION FROM FACIAL IMAGES
Full text linkIdentifying gender through facial images is a crucial aspect in various life contexts. Biometric technology, such as facial recognition, has become an integral part of various applications, including fraud detection, cybersecurity protection, and consumer behavior analysis. With the advancement of technology and the progress in artificial intelligence, especially through the use of Convolutional Neural Networks (CNNs), computers can now identify gender from facial images with a high level of accuracy. Although there are still some challenges, such as variations in pose, facial expressions, and different lighting conditions, CNNs can overcome these obstacles. This study uses the CelebA dataset, which consists of 122,000 facial images of both men and women. The dataset has been processed to maintain a balanced number of samples for each gender class, resulting in a total of 101,568 samples. The data is divided into training, validation, and test sets, with 80% used for training, and the remaining 20% split between validation and testing. Eight different CNN architectures are applied, including VGG16, VGG19, MobileNetV2, ResNet-50, ResNet-50 V2, Inception V3, Inception ResNet V2, and AlexNet. Although previous research has shown the potential of CNN architectures for various classification tasks, these studies often encounter issues of overfitting on large datasets, which can reduce model accuracy. This study applies dropout techniques and hyperparameter tuning to address overfitting issues and optimize model performance. The training results indicate that ResNet-50, ResNet-50 V2, and Inception V3 achieved the highest accuracy of 98%, while VGG16, VGG19, MobileNetV2, and AlexNet achieved accuracies of 95% and 97%, respectively. Performance evaluation using confusion matrices, precision, recall, and F1-score demonstrates excellent performance
SENTIMENT ANALYSIS OF PLAYER FEEDBACK IN ALGORUN: A STUDY OF DEEP LEARNING MODELS FOR GAME-BASED LEARNING
Full text linkAlgoRun: Coding Game is a game-based learning application aimed at teaching computational thinking (CT) concepts such as variables, conditions, loops, and functions. Evaluating user feedback in such educational games is challenging, as traditional sentiment analysis techniques often overlook nuanced responses. Despite its potential to inform content improvements, sentiment analysis in game-based learning remains underexplored. This study compares the performance of deep learning models—DNN, CNN, RNN with LSTM, and Bidirectional LSTM—for sentiment classification of AlgoRun user reviews, using TF-IDF and word embeddings as feature extraction methods. A total of 1,440 reviews were scraped from the Google Play Store, translated, and preprocessed using data preparation techniques (dropna, fillna), text preprocessing (case folding, cleaning, tokenization, stopword removal, stemming), and feature extraction (TF-IDF and word embeddings). The dataset was labeled into negative, neutral, and positive classes, and split 80% for training and 20% for testing. Among the tested models, the DNN with TF-IDF achieved the highest accuracy of 98.86%, followed by CNN with Word Embeddings (96.97%), Bidirectional LSTM (96.59%), and RNN with LSTM (92.42%). The DNN also showed stable performance and convergence at the 10th epoch, outperforming other models in precision, recall, and F1-score. These results suggest that DNN with TF-IDF is highly effective for sentiment classification in the context of game-based learning. The findings offer useful guidance for developers to adapt content and enhance game quality based on user feedback. This research also contributes to the growing body of literature on leveraging sentiment analysis to optimize educational applications
CLASSIFICATION OF NATURAL DISASTERS IN WEST SEMARANG BASED ON WEATHER DATA USING DEEP LEARNING
Full text linkNatural disasters like floods, landslides, and fires pose serious threats to both life and mental well-being, especially in vulnerable areas like West Semarang, which frequently experiences extreme weather. To mitigate these risks, an accurate classification system is essential for timely prevention and response. This study compares the performance of three neural network models—Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in classifying natural disasters using weather data. LSTM and GRU are particularly effective for handling long-term dependencies and addressing vanishing gradient problems common in time series data. Data for the study comes from the Semarang City Regional Disaster Management Agency (BPBD) and the Meteorology, Climatology, and Geophysics Agency (BMKG), spanning 2019 to 2022. The models achieved a high accuracy of 95.8%, but this is due to an imbalanced dataset—70 records of natural disasters versus 1377 without—resulting in classification favoring "no disaster." Among the models, LSTM performed the best, reaching optimal accuracy in just 20.0671 seconds per epoch. This suggests LSTM is the most effective model for this classification task